The Protozoan Acanthamoeba, Students and Contact Lenses

Abstract

The research (and paper following this) will be investigating the incidence of the protozoan Acanthamoeba in the storage cases of students’ contact lenses. It will be done to see if it is a significant cause of Acanthamoeba infections in such individuals. The object of data collection will be students who wear re-usable contact lenses. The data will be collected using confocal microscopy and a questionnaire. It would be useful to obtain these results since, if the alternative hypothesis is correct, information could be given to students to help reduce the cases of Acanthamoeba being in contact with contact lenses, to prevent the frequency of Acanthamoeba infections.

Introduction

The Centre for Disease Control and Prevention (2011) described Acanthamoeba infections as being “a rare but serious infection of the eye that can result in permanent visual impairment or blindness. This infection is caused by a microscopic, free-living ameba (single-celled living organism) called Acanthamoeba. Acanthamoeba causes Acanthamoeba Infections when it infects the transparent outer covering of the eye called the cornea. Acanthamoeba amoebas are very common in nature and can be found in bodies of water…soil, and air”.

A previous case-control study by Radford et al (1995), found that over 80% of Acanthamoeba keratitis could be avoided by the use of lens disinfectant, out of 35 case studies. A more recent case study (Mutoh et al, 2010) found similar results, when in 6 out of 9 cases the cause of the AcanthamoebaInfections was people washing their contact lens cases with tap water. Thus, it is presumable that such infections may be the cause of tap water contaminating the storage cases and so the contact lenses. This would lead to the production of a leading alternative hypothesis because, although past research may have been incorrect (e.g. the former is a little out-dated, while the latter has a small participant number) and is not based around the population group of students, it places any new discoveries made by the following research into context. Thus, a suitable leading alternative hypothesis is: The incidence of the protozoan Acanthamoeba in the storage cases of students who wear contact lenses, is a significant cause of Acanthamoeba infections in such individuals? A null hypothesis would be: The incidence of the protozoan Acanthamoeba in the storage cases of students who wear contact lenses, is not a significant cause of Acanthamoeba infections in such individuals?

Figure 1: Example Poster

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Figure 2: Example Questionnaire

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Methodology

I would use volunteer sampling to gather 100 participants, by placing posters around the Glyntaff, Trefforest and Atrium campuses [Figure 1]. The posters would advertise all the basic aspects of the experiment (minus the main aim so there will be no leading answers), detailing that it is for those who wear contact lenses and are students. As an incentive, there would be £3 food vouchers for each person who completes the experiment because although it is a useful reward for those who participate, it is not so great an incentive that people would give false answers to join it. Although this sampling type is limited because it means students of this particular university will join, lowering population validity, it will be an in depth study of this particular area. Also, if it is successful, it can be replicated in other universities.

The experiment will consist of a quick test for current Acanthamoeba Infections which may not have been detected yet, so that both past and present symptoms can be accounted for. This will be done by an eye specialist who will attempt to see possible ameba by confocal microscopy (Centre for Disease Control and Prevention, 2011). There will also be two controls of the experiment, using two confederates who have already been diagnosed with or without the infection by several other specialists. Then, there will be a short questionnaire which the participants will complete in silence [Figure 2]. To reduce the chance of confounding variables, there will be accurate control of such extraneous issues e.g. controlled temperature, same room, same questionnaire, same writing equipment, same seating conditions, same time of day. This will help to increase the validity of the data because the responses are gathered in a standardised way. It is also a useful method to use because a variation of objective and subjective questions can be picked, thus combining the richness of subjective data, with the ease of analysing the results from the objective data. In addition, it is relatively quick and easy to collect information, thus meaning a large sample size. Lastly, the participants will be reminded of the ethics of the experiment e.g. that they have the right to withdraw at any point, after the experiment they will be able to have full access and explanations of the results and all data will be kept anonymous.

Figure 3: Example of Points System on Questionnaire

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Figure 4: Example of Table of Results

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Results and Analysis

A score will be created from the objective answers given in the questionnaire (0-7), where the higher the number the more likely that particular student should have Acanthamoeba in their contact lenses case. This information will then by recorded in a table such as that in figure 3, along with severity and number of past and present infections. To present the data in a visually simplistic manner, I will plot the questionnaire scores against the number of infections in a scatter diagram, along with a line of best fit [Figure 4].

The data will then be tested to see if it significant. This will be done using the Chi-squared statistical test. This will be done using the formula, where O is the observed data (i.e. the difference between the questionnaire score and number of infections) and E is the expected data (i.e. there is no specific difference between the questionnaire score and number of infections). If there is a significance level of P<0.05 it means there is a 5% or less probability that the results were due to a factor other than the independent variable and the null hypothesis can be accepted, which will be calculated by using a critical value table of chi-squared distribution and working out the degree of freedom. Thus, this will tell us which hypothesis should be accepted and which should be rejected.